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https://issues.apache.org/jira/browse/SPARK-8881?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14618369#comment-14618369
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Nishkam Ravi commented on SPARK-8881:
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This isn't the best example because the third worker will get screened out.
Consider the following instead: three workers with num_cores (8, 8, 3).
spark.cores.maximum=8, spark.executor.cores=2. Core allocation would be (3, 3,
2). 3 executors launched instead of 4. You get the drift.
> Scheduling fails if num_executors < num_workers
> -----------------------------------------------
>
> Key: SPARK-8881
> URL: https://issues.apache.org/jira/browse/SPARK-8881
> Project: Spark
> Issue Type: Bug
> Components: Deploy
> Affects Versions: 1.4.0, 1.5.0
> Reporter: Nishkam Ravi
>
> Current scheduling algorithm (in Master.scala) has two issues:
> 1. cores are allocated one at a time instead of spark.executor.cores at a time
> 2. when spark.cores.max/spark.executor.cores < num_workers, executors are not
> launched and the app hangs (due to 1)
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